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Fuzzy based expert system for diagnosis of coronary artery disease in nigeria.
Muhammad, L J; Algehyne, Ebrahem A.
  • Muhammad LJ; Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria.
  • Algehyne EA; Department of Mathematics, University of Tabuk, Tabuk, 71491 Saudi Arabia.
Health Technol (Berl) ; 11(2): 319-329, 2021.
Article in English | MEDLINE | ID: covidwho-1092850
ABSTRACT
Expert system is an artificial intelligence based system that imitates the decision making ability of human and it is used as the diagnostic tool for many diseases including diabetes mellitus, COVID-19, cancers, coronary artery disease (CAD), among other diseases. Even though CAD is globally one of the deadliest diseases and it is not well known in Nigeria, it causes many deaths as such in 2014, 53,836 or 2.82% of total deaths in Nigeria resulted from the CAD. In this study, fuzzy based expert system for diagnosis of CAD is developed in order to provide the complementary diagnostic tools for diagnosis of CAD's patients in Nigeria. The improved C4.5 data mining algorithm is used to transfer the knowledge of human expert to the knowledge base on the expert system instead of using conventional techniques such as interviews, questionnaires, etc. Taken together, the performance evaluation system was carried out, and the system has an overall accuracy, sensitivity and specificity of 94.55%, 95.35% and 95.00% respectively; which show that, the system is reliable and capable of diagnosing both negative and positive cases of CAD patients efficiently.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Observational study / Reviews Language: English Journal: Health Technol (Berl) Year: 2021 Document Type: Article Affiliation country: S12553-021-00531-z

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Observational study / Reviews Language: English Journal: Health Technol (Berl) Year: 2021 Document Type: Article Affiliation country: S12553-021-00531-z